Coarse-grained and fine-grained parallel optimization for real-time en-face OCT imaging

This paper presents parallel optimizations in the en-face (C-scan) optical coherence tomography (OCT) display. Compared with the cross-sectional (B-scan) imagery, the production of en-face images is more computationally demanding, due to the increased size of the data handled by the digital signal processing (DSP) algorithms. A sequential implementation of the DSP leads to a limited number of real-time generated en-face images. There are OCT applications, where simultaneous production of large number of en-face images from multiple depths is required, such as real-time diagnostics and monitoring of surgery and ablation. In sequential computing, this requirement leads to a significant increase of the time to process the data and to generate the images. As a result, the processing time exceeds the acquisition time and the image generation is not in real-time. In these cases, not producing en-face images in real-time makes the OCT system ineffective. Parallel optimization of the DSP algorithms provides a solution to this problem. Coarse-grained central processing unit (CPU) based and fine-grained graphics processing unit (GPU) based parallel implementations of the conventional Fourier domain (CFD) OCT method and the Master-Slave Interferometry (MSI) OCT method are studied. In the coarse-grained CPU implementation, each parallel thread processes the whole OCT frame and generates a single en-face image. The corresponding fine-grained GPU implementation launches one parallel thread for every data point from the OCT frame and thus achieves maximum parallelism. The performance and scalability of the CPU-based and GPU-based parallel approaches are analyzed and compared. The quality and the resolution of the images generated by the CFD method and the MSI method are also discussed and compared.